Experiment with DDA
## IDjoueur nom_du_joueur heure_connexion_joueur nom_du_jeu
## 1: 02fmwoq6z Lina Debien 10_25_2016_17h33m45s Logique2
## 2: 02fmwoq6z Lina Debien 10_25_2016_17h33m45s Logique2
## 3: 02fmwoq6z Lina Debien 10_25_2016_17h33m45s Logique2
## 4: 02fmwoq6z Lina Debien 10_25_2016_17h33m45s Logique2
## 5: 02fmwoq6z Lina Debien 10_25_2016_17h33m45s Logique2
## ---
## 6986: ysuq0jc98 Martin Lanchon 10_26_2016_16h00m21s Motrice
## 6987: ysuq0jc98 Martin Lanchon 10_26_2016_16h00m21s Motrice
## 6988: ysuq0jc98 Martin Lanchon 10_26_2016_16h00m21s Motrice
## 6989: ysuq0jc98 Martin Lanchon 10_26_2016_16h00m21s Motrice
## 6990: ysuq0jc98 Martin Lanchon 10_26_2016_16h00m21s Motrice
## action_de_jeu mise difficulty moutons_sauves moutons_tues score
## 1: 1 2 0.0 0 2 -2
## 2: 2 5 0.0 5 2 3
## 3: 3 1 0.1 5 3 2
## 4: 4 7 0.0 12 3 9
## 5: 5 7 0.1 12 10 2
## ---
## 6986: 26 3 0.5 69 35 34
## 6987: 27 1 0.6 69 36 33
## 6988: 28 7 0.5 76 36 40
## 6989: 29 1 0.6 76 37 39
## 6990: 30 7 0.5 83 37 46
## gagnant horodateur prenomNom age sexe langueMaternelle
## 1: 0 10/25/2016 17:41:48 Lina Debien 14 1 1
## 2: 1 10/25/2016 17:41:48 Lina Debien 14 1 1
## 3: 0 10/25/2016 17:41:48 Lina Debien 14 1 1
## 4: 1 10/25/2016 17:41:48 Lina Debien 14 1 1
## 5: 0 10/25/2016 17:41:48 Lina Debien 14 1 1
## ---
## 6986: 1 10/26/2016 16:06:56 Martin Lanchon 17 0 1
## 6987: 0 10/26/2016 16:06:56 Martin Lanchon 17 0 1
## 6988: 1 10/26/2016 16:06:56 Martin Lanchon 17 0 1
## 6989: 0 10/26/2016 16:06:56 Martin Lanchon 17 0 1
## 6990: 1 10/26/2016 16:06:56 Martin Lanchon 17 0 1
## niveauEtude profilJoueur8
## 1: 1 0
## 2: 1 0
## 3: 1 0
## 4: 1 0
## 5: 1 0
## ---
## 6986: 1 1
## 6987: 1 1
## 6988: 1 1
## 6989: 1 1
## 6990: 1 1
## jeuxFav
## 1: géometrie dash /color switch/fruit ninja sur téléphone
## 2: géometrie dash /color switch/fruit ninja sur téléphone
## 3: géometrie dash /color switch/fruit ninja sur téléphone
## 4: géometrie dash /color switch/fruit ninja sur téléphone
## 5: géometrie dash /color switch/fruit ninja sur téléphone
## ---
## 6986: Jeu vidéo : League of Legend\nJeu vidéo : Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6987: Jeu vidéo : League of Legend\nJeu vidéo : Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6988: Jeu vidéo : League of Legend\nJeu vidéo : Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6989: Jeu vidéo : League of Legend\nJeu vidéo : Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6990: Jeu vidéo : League of Legend\nJeu vidéo : Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## autoEffJoueur1 autoEffJoueur2 autoEffJoueur3 autoEffJoueur4
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 6986: 5 4 5 5
## 6987: 5 4 5 5
## 6988: 5 4 5 5
## 6989: 5 4 5 5
## 6990: 5 4 5 5
## autoEffJoueur5 autoEffJoueur6 autoEffJoueur7 autoEffJoueur8
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 6986: 5 5 5 5
## 6987: 5 5 5 5
## 6988: 5 5 5 5
## 6989: 5 5 5 5
## 6990: 5 5 5 5
## autoEffJoueur9 autoEffJoueur10 loterie1 loterie2 loterie3 loterie4
## 1: NA NA 1 1 1 1
## 2: NA NA 1 1 1 1
## 3: NA NA 1 1 1 1
## 4: NA NA 1 1 1 1
## 5: NA NA 1 1 1 1
## ---
## 6986: 4 4 1 1 1 1
## 6987: 4 4 1 1 1 1
## 6988: 4 4 1 1 1 1
## 6989: 4 4 1 1 1 1
## 6990: 4 4 1 1 1 1
## loterie5 loterie6 loterie7 loterie8 loterie9 loterie10
## 1: 0 0 0 0 0 0
## 2: 0 0 0 0 0 0
## 3: 0 0 0 0 0 0
## 4: 0 0 0 0 0 0
## 5: 0 0 0 0 0 0
## ---
## 6986: 1 1 1 1 1 1
## 6987: 1 1 1 1 1 1
## 6988: 1 1 1 1 1 1
## 6989: 1 1 1 1 1 1
## 6990: 1 1 1 1 1 1
## play.video.games play.board.games play.money.games self.eff
## 1: 1 0.4 0.3333333 NA
## 2: 1 0.4 0.3333333 NA
## 3: 1 0.4 0.3333333 NA
## 4: 1 0.4 0.3333333 NA
## 5: 1 0.4 0.3333333 NA
## ---
## 6986: 1 0.6 0.3333333 4.7
## 6987: 1 0.6 0.3333333 4.7
## 6988: 1 0.6 0.3333333 4.7
## 6989: 1 0.6 0.3333333 4.7
## 6990: 1 0.6 0.3333333 4.7Removing Outliers
## [1] "Outliers : 135499aaw, 9l7s14ocz, 9l7s14ocz, g6m2iu73e, lpc2zjkex, srn0c21wi"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : at13n1mb2, srn0c21wi"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : 2lvqyyzt9, 3t1l09dyk, e0tdz7cvh"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 9"## [1] "Total number of outliers motor task: 1"## [1] "Total number of outliers perceptive task: 3"## [1] "Total number of outliers logical task: 6"Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 3151.9 3174.9 -1571.9 3143.9 2336
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9957 -0.8530 -0.5675 0.9526 2.1623
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.6875 0.8292
## Number of obs: 2340, groups: IDjoueur, 78
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1106 0.1603 -6.928 4.27e-12 ***
## difficulty 3.2297 0.3438 9.395 < 2e-16 ***
## timeNorm -0.4618 0.1650 -2.798 0.00514 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.585
## timeNorm -0.216 -0.401## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 2340 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-2.126430
## 1st Qu.:-0.347466
## Median : 0.049837
## Mean : 0.003376
## 3rd Qu.: 0.448296
## Max. : 1.960052
## [1] "Intercept: -1.11 4.3e-12 ***"
## [1] "Difficulty: 3.23 5.7e-21 ***"
## [1] "Time: -0.462 0.0051 **"
## [1] "R2 fixed: 0.13"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.61"
## [1] "AIC: 3200"## 0% 25% 50% 75% 100%
## -1.96005180 -0.44829605 -0.04983725 0.34746630 2.12642981
## 0% 25% 50% 75% 100%
## -1.96005180 -0.44829605 -0.04983725 0.34746630 2.12642981
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2532.7 2555.3 -1262.3 2524.7 2126
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5031 -0.7242 -0.3428 0.8058 3.7576
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5808 0.7621
## Number of obs: 2130, groups: IDjoueur, 71
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1935 0.1751 -12.529 <2e-16 ***
## difficulty 9.0967 0.5527 16.459 <2e-16 ***
## timeNorm -0.3650 0.1824 -2.001 0.0454 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.615
## timeNorm -0.317 -0.299## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 2130
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.476587
## 1st Qu.:-0.370451
## Median :-0.036883
## Mean : 0.003092
## 3rd Qu.: 0.449790
## Max. : 1.689450
## [1] "Intercept: -2.19 5.2e-36 ***"
## [1] "Difficulty: 9.1 7.2e-61 ***"
## [1] "Time: -0.365 0.045 *"
## [1] "R2 fixed: 0.31"
## [1] "R2 mixed: 0.42"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2500"## 0% 25% 50% 75% 100%
## -1.68944968 -0.44979001 0.03688316 0.37045061 1.47658650
## 0% 25% 50% 75% 100%
## -1.68944968 -0.44979001 0.03688316 0.37045061 1.47658650
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2839.0 2861.8 -1415.5 2831.0 2216
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7599 -0.7933 -0.4388 0.8894 5.9937
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.538 1.24
## Number of obs: 2220, groups: IDjoueur, 74
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6820 0.1962 -8.574 < 2e-16 ***
## difficulty 4.8761 0.3902 12.497 < 2e-16 ***
## timeNorm -1.0018 0.2129 -4.707 2.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.435
## timeNorm -0.095 -0.617## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 2220 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-2.40565
## 1st Qu.:-0.91465
## Median :-0.21046
## Mean : 0.00762
## 3rd Qu.: 1.01654
## Max. : 2.33338
## [1] "Intercept: -1.68 1e-17 ***"
## [1] "Difficulty: 4.88 7.8e-36 ***"
## [1] "Time: -1 2.5e-06 ***"
## [1] "R2 fixed: 0.25"
## [1] "R2 mixed: 0.49"
## [1] "Cross Val: 0.66"
## [1] "AIC: 2800"## 0% 25% 50% 75% 100%
## -2.3333793 -1.0165435 0.2104629 0.9146509 2.4056548
## 0% 25% 50% 75% 100%
## -2.3333793 -1.0165435 0.2104629 0.9146509 2.4056548
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3248, p-value = 0.1852
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.11704
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.27298, p-value = 0.7849
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02518738
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.65099, p-value = 0.5151
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05892853Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.80811, p-value = 0.419
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.0708607
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1157, p-value = 0.2645
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1023279
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1544, p-value = 0.2483
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1039873Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 36 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1205, p-value = 0.2625
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1226574## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 33 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3508, p-value = 0.1768
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1562583## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 35 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.83788, p-value = 0.4021
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09546528Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.52666, p-value = 0.5984
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04333044
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 3.3093, p-value = 0.0009353
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2869732
##
## [1] "risk.av.on.level.s 0.29 0.00094 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 3.2974, p-value = 0.000976
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2789266
##
## [1] "risk.av.on.level.l 0.28 0.00098 ***"Age and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.55202, p-value = 0.5809
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04421776
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.47488, p-value = 0.6349
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03986734
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.30538, p-value = 0.7601
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02507917Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -4.2075, p-value = 2.583e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.3939997
##
## [1] "sexe.on.level.m -0.39 2.6e-05 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.0835, p-value = 0.2786
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1064806
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.5838, p-value = 0.009772
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2485807
##
## [1] "sexe.on.level.l -0.25 0.0098 **"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 225, p-value = 2.648e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.0356472 -0.3746198
## sample estimates:
## difference in location
## -0.6783409
##
## [1] "sexe.on.level.m.2 -0.68 2.6e-05 *** mean(A): 0.2 mean(B): -0.56"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 439, p-value = 0.2814
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5476773 0.1860382
## sample estimates:
## difference in location
## -0.1767853
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 341, p-value = 0.009943
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.4009396 -0.1989844
## sample estimates:
## difference in location
## -0.8165971
##
## [1] "sexe.on.level.l.2 -0.82 0.0099 ** mean(A): 0.2 mean(B): -0.54"Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.20871, p-value = 0.8347
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01082703Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.2833, p-value = 0.1994
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.066153In game level and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.65283, p-value = 0.5139
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02949556
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.15101, p-value = 0.88
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01165501
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5834, p-value = 0.1133
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1283702
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.37801, p-value = 0.7054
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02998889Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.64555, p-value = 0.5186
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0356036
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.50129, p-value = 0.6162
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0469424
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.34017, p-value = 0.7337
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03342996
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.25778, p-value = 0.7966
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02480039
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 4760, p-value = 0.5193
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.05522880 0.02878889
## sample estimates:
## difference in location
## -0.01285877
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 554, p-value = 0.6201
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.09035599 0.06089351
## sample estimates:
## difference in location
## -0.01840847
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 498, p-value = 0.7385
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.08529737 0.06934092
## sample estimates:
## difference in location
## -0.01135922
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 535, p-value = 0.8012
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.08451038 0.07413238
## sample estimates:
## difference in location
## -0.009591776Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.12516, p-value = 0.9004
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.006065128
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.079, p-value = 0.937
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.006499567
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.39469, p-value = 0.6931
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03422617
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.52264, p-value = 0.6012
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04421026Self efficacy and subjective difficulty error
## Warning: Removed 104 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.32517, p-value = 0.7451
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02093754## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 36 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.14142, p-value = 0.8875
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01548103## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 33 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.012624, p-value = 0.9899
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.001460358## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 35 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.4493, p-value = 0.6532
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.05119152OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 27 3e-04 ***
## 2: 0.09375 -0.0220 59 0.035 *
## 3: 0.15625 -0.0250 68 0.32 :(
## 4: 0.21875 -0.0045 72 0.75 :(
## 5: 0.28125 -0.0260 77 0.29 :(
## 6: 0.34375 -0.0290 80 0.23 :(
## 7: 0.40625 -0.0340 80 0.18 :(
## 8: 0.46875 -0.0750 80 0.0067 **
## 9: 0.53125 -0.1200 80 4.8e-06 ***
## 10: 0.59375 -0.1300 78 4.8e-06 ***
## 11: 0.65625 -0.2200 79 2.4e-09 ***
## 12: 0.71875 -0.2200 76 3.1e-09 ***
## 13: 0.78125 -0.2500 51 9e-08 ***
## 14: 0.84375 -0.2400 56 3.3e-06 ***
## 15: 0.90625 -0.1900 30 1.2e-06 ***
## 16: 0.96875 -0.2300 17 0.00023 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 27 3e-04 ***
## 2: 59 0.035 *
## 3: 68 0.32 :(
## 4: 72 0.75 :(
## 5: 77 0.29 :(
## 6: 80 0.23 :(
## 7: 80 0.18 :(
## 8: 80 0.0067 **
## 9: 80 4.8e-06 ***
## 10: 78 4.8e-06 ***
## 11: 79 2.4e-09 ***
## 12: 76 3.1e-09 ***
## 13: 51 9e-08 ***
## 14: 56 3.3e-06 ***
## 15: 30 1.2e-06 ***
## 16: 17 0.00023 ***
## [1] 63.1
## [1] 21.3
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 22 0.003 **
## 2: 0.09375 -0.0045 30 0.93 :(
## 3: 0.15625 0.0045 32 0.86 :(
## 4: 0.21875 0.0210 35 0.65 :(
## 5: 0.28125 0.0480 34 0.16 :(
## 6: 0.34375 -0.0200 33 0.73 :(
## 7: 0.40625 -0.0430 34 0.22 :(
## 8: 0.46875 -0.0880 34 0.073 .
## 9: 0.53125 -0.1200 33 0.017 *
## 10: 0.59375 -0.1700 33 0.0027 **
## 11: 0.65625 -0.2100 32 0.0018 **
## 12: 0.71875 -0.2200 26 0.0034 **
## 13: 0.78125 -0.2100 14 0.032 *
## 14: 0.84375 -0.3100 9 0.024 *
## 15: 0.90625 -0.1900 6 0.036 *
## 16: 0.96875 NA 4 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 22 0.003 **
## 2: 30 0.93 :(
## 3: 32 0.86 :(
## 4: 35 0.65 :(
## 5: 34 0.16 :(
## 6: 33 0.73 :(
## 7: 34 0.22 :(
## 8: 34 0.073 .
## 9: 33 0.017 *
## 10: 33 0.0027 **
## 11: 32 0.0018 **
## 12: 26 0.0034 **
## 13: 14 0.032 *
## 14: 9 0.024 *
## 15: 6 0.036 *
## [1] 27.1
## [1] 9.78## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 7 NA
## 2: 0.09375 -0.094 44 0.00048 ***
## 3: 0.15625 -0.044 42 0.13 :(
## 4: 0.21875 -0.076 48 0.081 .
## 5: 0.28125 -0.072 60 0.012 *
## 6: 0.34375 -0.042 66 0.1 :(
## 7: 0.40625 -0.037 67 0.29 :(
## 8: 0.46875 -0.088 64 0.022 *
## 9: 0.53125 -0.140 65 9.8e-05 ***
## 10: 0.59375 -0.170 62 0.00029 ***
## 11: 0.65625 -0.230 59 2.6e-06 ***
## 12: 0.71875 -0.220 52 4.8e-05 ***
## 13: 0.78125 -0.210 33 4.1e-05 ***
## 14: 0.84375 -0.240 30 0.00025 ***
## 15: 0.90625 -0.160 17 0.00023 ***
## 16: 0.96875 -0.330 9 0.008 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.00048 ***
## 2: 42 0.13 :(
## 3: 48 0.081 .
## 4: 60 0.012 *
## 5: 66 0.1 :(
## 6: 67 0.29 :(
## 7: 64 0.022 *
## 8: 65 9.8e-05 ***
## 9: 62 0.00029 ***
## 10: 59 2.6e-06 ***
## 11: 52 4.8e-05 ***
## 12: 33 4.1e-05 ***
## 13: 30 0.00025 ***
## 14: 17 0.00023 ***
## 15: 9 0.008 **
## [1] 47.9
## [1] 18.5## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 7 NA
## 3: 0.15625 -0.057 23 0.092 .
## 4: 0.21875 -0.076 21 0.25 :(
## 5: 0.28125 -0.041 32 0.38 :(
## 6: 0.34375 -0.076 33 0.086 .
## 7: 0.40625 -0.049 33 0.25 :(
## 8: 0.46875 -0.130 36 0.0067 **
## 9: 0.53125 -0.087 36 0.014 *
## 10: 0.59375 -0.140 34 0.0041 **
## 11: 0.65625 -0.230 32 2.9e-05 ***
## 12: 0.71875 -0.240 33 0.00014 ***
## 13: 0.78125 -0.280 19 0.00083 ***
## 14: 0.84375 -0.220 20 0.14 :(
## 15: 0.90625 -0.220 7 0.02 *
## 16: 0.96875 -0.160 4 0.098 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 23 0.092 .
## 2: 21 0.25 :(
## 3: 32 0.38 :(
## 4: 33 0.086 .
## 5: 33 0.25 :(
## 6: 36 0.0067 **
## 7: 36 0.014 *
## 8: 34 0.0041 **
## 9: 32 2.9e-05 ***
## 10: 33 0.00014 ***
## 11: 19 0.00083 ***
## 12: 20 0.14 :(
## 13: 7 0.02 *
## 14: 4 0.098 .
## [1] 25.9
## [1] 10.5## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 -0.0130 13 0.78 :(
## 4: 0.21875 -0.0045 29 0.21 :(
## 5: 0.28125 -0.0310 61 0.5 :(
## 6: 0.34375 -0.0720 73 0.025 *
## 7: 0.40625 -0.0810 74 0.025 *
## 8: 0.46875 -0.1000 74 0.0053 **
## 9: 0.53125 -0.1300 76 0.00052 ***
## 10: 0.59375 -0.2000 72 2.8e-06 ***
## 11: 0.65625 -0.2500 61 2.2e-06 ***
## 12: 0.71875 -0.2900 38 1.6e-05 ***
## 13: 0.78125 -0.4100 11 0.0065 **
## 14: 0.84375 -0.5400 4 0.2 :(
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 13 0.78 :(
## 2: 29 0.21 :(
## 3: 61 0.5 :(
## 4: 73 0.025 *
## 5: 74 0.025 *
## 6: 74 0.0053 **
## 7: 76 0.00052 ***
## 8: 72 2.8e-06 ***
## 9: 61 2.2e-06 ***
## 10: 38 1.6e-05 ***
## 11: 11 0.0065 **
## 12: 4 0.2 :(
## [1] 48.8
## [1] 28.1## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 -0.013 10 0.76 :(
## 4: 0.21875 0.019 18 0.79 :(
## 5: 0.28125 0.076 19 0.095 .
## 6: 0.34375 0.031 19 0.59 :(
## 7: 0.40625 -0.035 19 0.56 :(
## 8: 0.46875 -0.064 19 0.36 :(
## 9: 0.53125 -0.100 18 0.12 :(
## 10: 0.59375 -0.130 17 0.072 .
## 11: 0.65625 -0.220 12 0.054 .
## 12: 0.71875 -0.230 7 0.55 :(
## 13: 0.78125 NA 1 NA
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 10 0.76 :(
## 2: 18 0.79 :(
## 3: 19 0.095 .
## 4: 19 0.59 :(
## 5: 19 0.56 :(
## 6: 19 0.36 :(
## 7: 18 0.12 :(
## 8: 17 0.072 .
## 9: 12 0.054 .
## 10: 7 0.55 :(
## [1] 15.8
## [1] 4.44## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 -0.220 11 0.07 .
## 5: 0.28125 -0.067 42 0.16 :(
## 6: 0.34375 -0.084 49 0.031 *
## 7: 0.40625 -0.089 49 0.035 *
## 8: 0.46875 -0.110 49 0.018 *
## 9: 0.53125 -0.140 49 0.0032 **
## 10: 0.59375 -0.240 45 5.8e-05 ***
## 11: 0.65625 -0.260 39 0.00018 ***
## 12: 0.71875 -0.380 23 0.00024 ***
## 13: 0.78125 -0.410 6 0.031 *
## 14: 0.84375 NA 1 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.07 .
## 2: 42 0.16 :(
## 3: 49 0.031 *
## 4: 49 0.035 *
## 5: 49 0.018 *
## 6: 49 0.0032 **
## 7: 45 5.8e-05 ***
## 8: 39 0.00018 ***
## 9: 23 0.00024 ***
## 10: 6 0.031 *
## [1] 36.2
## [1] 16.7## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 5 NA
## 7: 0.40625 -0.09 6 0.44 :(
## 8: 0.46875 -0.14 6 0.31 :(
## 9: 0.53125 -0.12 9 0.41 :(
## 10: 0.59375 -0.18 10 0.15 :(
## 11: 0.65625 -0.30 10 0.041 *
## 12: 0.71875 -0.29 8 0.042 *
## 13: 0.78125 -0.57 4 0.095 .
## 14: 0.84375 NA 3 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 6 0.44 :(
## 2: 6 0.31 :(
## 3: 9 0.41 :(
## 4: 10 0.15 :(
## 5: 10 0.041 *
## 6: 8 0.042 *
## 7: 4 0.095 .
## [1] 7.57
## [1] 2.3## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 15 NA
## 2: 0.09375 -0.094 54 5.7e-07 ***
## 3: 0.15625 -0.095 61 1.7e-06 ***
## 4: 0.21875 -0.100 43 0.00062 ***
## 5: 0.28125 -0.094 51 0.017 *
## 6: 0.34375 -0.120 46 0.0058 **
## 7: 0.40625 -0.049 41 0.44 :(
## 8: 0.46875 -0.110 45 0.0057 **
## 9: 0.53125 -0.150 48 0.00038 ***
## 10: 0.59375 -0.079 37 0.094 .
## 11: 0.65625 -0.140 40 0.0036 **
## 12: 0.71875 -0.180 53 4.8e-05 ***
## 13: 0.78125 -0.140 33 0.00023 ***
## 14: 0.84375 -0.150 44 0.0018 **
## 15: 0.90625 -0.160 29 1.7e-06 ***
## 16: 0.96875 -0.230 17 0.00023 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 54 5.7e-07 ***
## 2: 61 1.7e-06 ***
## 3: 43 0.00062 ***
## 4: 51 0.017 *
## 5: 46 0.0058 **
## 6: 41 0.44 :(
## 7: 45 0.0057 **
## 8: 48 0.00038 ***
## 9: 37 0.094 .
## 10: 40 0.0036 **
## 11: 53 4.8e-05 ***
## 12: 33 0.00023 ***
## 13: 44 0.0018 **
## 14: 29 1.7e-06 ***
## 15: 17 0.00023 ***
## [1] 42.8
## [1] 10.9## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 9 NA
## 2: 0.09375 -0.094 9 0.13 :(
## 3: 0.15625 NA 9 NA
## 4: 0.21875 -0.160 5 0.054 .
## 5: 0.28125 -0.067 5 0.31 :(
## 6: 0.34375 -0.085 4 0.38 :(
## 7: 0.40625 -0.049 5 1 :(
## 8: 0.46875 -0.100 5 0.81 :(
## 9: 0.53125 -0.200 5 0.44 :(
## 10: 0.59375 -0.097 4 0.88 :(
## 11: 0.65625 -0.013 6 1 :(
## 12: 0.71875 -0.270 7 0.075 .
## 13: 0.78125 -0.085 4 0.58 :(
## 14: 0.84375 -0.200 6 0.14 :(
## 15: 0.90625 -0.190 6 0.036 *
## 16: 0.96875 NA 4 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.13 :(
## 2: 5 0.054 .
## 3: 5 0.31 :(
## 4: 4 0.38 :(
## 5: 5 1 :(
## 6: 5 0.81 :(
## 7: 5 0.44 :(
## 8: 4 0.88 :(
## 9: 6 1 :(
## 10: 7 0.075 .
## 11: 4 0.58 :(
## 12: 6 0.14 :(
## 13: 6 0.036 *
## [1] 5.46
## [1] 1.39## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 6 NA
## 2: 0.09375 -0.094 38 2.2e-06 ***
## 3: 0.15625 -0.110 32 0.00024 ***
## 4: 0.21875 -0.150 24 0.00073 ***
## 5: 0.28125 -0.110 25 0.036 *
## 6: 0.34375 -0.094 27 0.055 .
## 7: 0.40625 -0.085 24 0.27 :(
## 8: 0.46875 -0.120 22 0.055 .
## 9: 0.53125 -0.180 27 0.0032 **
## 10: 0.59375 -0.130 22 0.038 *
## 11: 0.65625 -0.270 20 0.00072 ***
## 12: 0.71875 -0.170 26 0.0041 **
## 13: 0.78125 -0.210 21 0.00093 ***
## 14: 0.84375 -0.130 23 0.0046 **
## 15: 0.90625 -0.120 16 0.00033 ***
## 16: 0.96875 -0.330 9 0.008 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 38 2.2e-06 ***
## 2: 32 0.00024 ***
## 3: 24 0.00073 ***
## 4: 25 0.036 *
## 5: 27 0.055 .
## 6: 24 0.27 :(
## 7: 22 0.055 .
## 8: 27 0.0032 **
## 9: 22 0.038 *
## 10: 20 0.00072 ***
## 11: 26 0.0041 **
## 12: 21 0.00093 ***
## 13: 23 0.0046 **
## 14: 16 0.00033 ***
## 15: 9 0.008 **
## [1] 23.7
## [1] 6.57## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 7 NA
## 3: 0.15625 -0.0540 20 0.12 :(
## 4: 0.21875 -0.0044 14 0.95 :(
## 5: 0.28125 -0.0790 21 0.27 :(
## 6: 0.34375 -0.1300 15 0.1 :(
## 7: 0.40625 0.0220 12 0.56 :(
## 8: 0.46875 -0.1300 18 0.081 .
## 9: 0.53125 -0.0870 16 0.093 .
## 10: 0.59375 0.0130 11 0.89 :(
## 11: 0.65625 -0.0130 14 0.66 :(
## 12: 0.71875 -0.2000 20 0.038 *
## 13: 0.78125 -0.1400 8 0.23 :(
## 14: 0.84375 -0.1700 15 0.79 :(
## 15: 0.90625 -0.2200 7 0.02 *
## 16: 0.96875 -0.1600 4 0.098 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 20 0.12 :(
## 2: 14 0.95 :(
## 3: 21 0.27 :(
## 4: 15 0.1 :(
## 5: 12 0.56 :(
## 6: 18 0.081 .
## 7: 16 0.093 .
## 8: 11 0.89 :(
## 9: 14 0.66 :(
## 10: 20 0.038 *
## 11: 8 0.23 :(
## 12: 15 0.79 :(
## 13: 7 0.02 *
## 14: 4 0.098 .
## [1] 13.9
## [1] 5.12## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 17 0.027 *
## 2: 0.09375 0.025 38 0.69 :(
## 3: 0.15625 0.058 51 0.15 :(
## 4: 0.21875 0.067 61 0.17 :(
## 5: 0.28125 0.040 66 0.11 :(
## 6: 0.34375 0.034 71 0.4 :(
## 7: 0.40625 0.010 71 0.83 :(
## 8: 0.46875 -0.062 72 0.054 .
## 9: 0.53125 -0.091 73 0.014 *
## 10: 0.59375 -0.130 67 0.0016 **
## 11: 0.65625 -0.230 63 4.3e-07 ***
## 12: 0.71875 -0.210 52 1.7e-05 ***
## 13: 0.78125 -0.280 30 0.00012 ***
## 14: 0.84375 -0.420 14 0.0012 **
## 15: 0.90625 NA 1 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 17 0.027 *
## 2: 38 0.69 :(
## 3: 51 0.15 :(
## 4: 61 0.17 :(
## 5: 66 0.11 :(
## 6: 71 0.4 :(
## 7: 71 0.83 :(
## 8: 72 0.054 .
## 9: 73 0.014 *
## 10: 67 0.0016 **
## 11: 63 4.3e-07 ***
## 12: 52 1.7e-05 ***
## 13: 30 0.00012 ***
## 14: 14 0.0012 **
## [1] 53.3
## [1] 20.6## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 16 0.04 *
## 2: 0.09375 0.025 25 0.76 :(
## 3: 0.15625 0.130 25 0.23 :(
## 4: 0.21875 0.100 25 0.11 :(
## 5: 0.28125 0.040 24 0.47 :(
## 6: 0.34375 -0.058 25 0.48 :(
## 7: 0.40625 -0.049 24 0.38 :(
## 8: 0.46875 -0.110 25 0.021 *
## 9: 0.53125 -0.120 24 0.047 *
## 10: 0.59375 -0.170 23 0.0048 **
## 11: 0.65625 -0.230 22 0.002 **
## 12: 0.71875 -0.290 19 0.002 **
## 13: 0.78125 -0.280 10 0.041 *
## 14: 0.84375 -0.450 4 0.098 .
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 16 0.04 *
## 2: 25 0.76 :(
## 3: 25 0.23 :(
## 4: 25 0.11 :(
## 5: 24 0.47 :(
## 6: 25 0.48 :(
## 7: 24 0.38 :(
## 8: 25 0.021 *
## 9: 24 0.047 *
## 10: 23 0.0048 **
## 11: 22 0.002 **
## 12: 19 0.002 **
## 13: 10 0.041 *
## 14: 4 0.098 .
## [1] 20.8
## [1] 6.51## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 0.013 13 0.83 :(
## 3: 0.15625 0.058 22 0.23 :(
## 4: 0.21875 0.140 24 0.083 .
## 5: 0.28125 0.058 24 0.14 :(
## 6: 0.34375 0.150 24 0.038 *
## 7: 0.40625 0.094 23 0.091 .
## 8: 0.46875 0.031 23 0.7 :(
## 9: 0.53125 -0.013 24 0.92 :(
## 10: 0.59375 -0.022 22 0.72 :(
## 11: 0.65625 -0.130 22 0.096 .
## 12: 0.71875 -0.040 19 0.36 :(
## 13: 0.78125 -0.170 11 0.067 .
## 14: 0.84375 -0.430 7 0.034 *
## 15: 0.90625 NA 1 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 13 0.83 :(
## 2: 22 0.23 :(
## 3: 24 0.083 .
## 4: 24 0.14 :(
## 5: 24 0.038 *
## 6: 23 0.091 .
## 7: 23 0.7 :(
## 8: 24 0.92 :(
## 9: 22 0.72 :(
## 10: 22 0.096 .
## 11: 19 0.36 :(
## 12: 11 0.067 .
## 13: 7 0.034 *
## [1] 19.8
## [1] 5.73## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 -0.060 4 0.36 :(
## 4: 0.21875 -0.160 12 0.21 :(
## 5: 0.28125 0.033 18 0.51 :(
## 6: 0.34375 -0.010 22 0.82 :(
## 7: 0.40625 -0.013 24 0.68 :(
## 8: 0.46875 -0.088 24 0.079 .
## 9: 0.53125 -0.110 25 0.038 *
## 10: 0.59375 -0.200 22 0.01 *
## 11: 0.65625 -0.290 19 0.00023 ***
## 12: 0.71875 -0.290 14 0.0024 **
## 13: 0.78125 -0.350 9 0.0084 **
## 14: 0.84375 NA 3 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 4 0.36 :(
## 2: 12 0.21 :(
## 3: 18 0.51 :(
## 4: 22 0.82 :(
## 5: 24 0.68 :(
## 6: 24 0.079 .
## 7: 25 0.038 *
## 8: 22 0.01 *
## 9: 19 0.00023 ***
## 10: 14 0.0024 **
## 11: 9 0.0084 **
## [1] 17.5
## [1] 6.93## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.47512 -0.32099 0.00198 0.28034 0.62514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17124 0.02662 6.432 1.52e-10 ***
## timeNorm 0.01183 0.02348 0.504 0.614
## obj.diff -0.63770 0.05194 -12.277 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1062912)
##
## Null deviance: 264.48 on 2339 degrees of freedom
## Residual deviance: 248.40 on 2337 degrees of freedom
## AIC: 1400.3
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73012 -0.23623 -0.06378 0.27512 0.77690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03288 0.01832 -1.795 0.0728 .
## timeNorm 0.05464 0.02474 2.209 0.0273 *
## obj.diff -0.23453 0.03151 -7.443 1.43e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1028656)
##
## Null deviance: 224.52 on 2129 degrees of freedom
## Residual deviance: 218.80 on 2127 degrees of freedom
## AIC: 1205.3
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.57929 -0.31805 -0.00183 0.31692 0.63841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17798 0.01962 9.072 < 2e-16 ***
## timeNorm 0.07804 0.02588 3.016 0.00259 **
## obj.diff -0.60382 0.04194 -14.398 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1105958)
##
## Null deviance: 268.47 on 2219 degrees of freedom
## Residual deviance: 245.19 on 2217 degrees of freedom
## AIC: 1416.9
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2887668 0.4250930 -0.13953917 234 8.3e-09 ***
## 2: 4.5 0.3577534 0.4490995 -0.09225023 234 0.00012 ***
## 3: 7.5 0.3736264 0.4588281 -0.08415455 234 0.0011 **
## 4: 10.5 0.3443223 0.4590774 -0.11697149 234 1.4e-06 ***
## 5: 13.5 0.3388278 0.4603425 -0.12311547 234 1.6e-07 ***
## 6: 16.5 0.3504274 0.4656950 -0.11694812 234 1.1e-06 ***
## 7: 19.5 0.3284493 0.4694363 -0.14111674 234 1.2e-09 ***
## 8: 22.5 0.3431013 0.4793797 -0.14609825 234 4.7e-09 ***
## 9: 25.5 0.3833944 0.4792794 -0.09957072 234 0.00013 ***
## 10: 28.5 0.3363858 0.4675171 -0.12098146 234 4.9e-08 ***
## time error.diff shapes
## 1: 1.5 -0.13953917 24
## 2: 4.5 -0.09225023 24
## 3: 7.5 -0.08415455 24
## 4: 10.5 -0.11697149 24
## 5: 13.5 -0.12311547 24
## 6: 16.5 -0.11694812 24
## 7: 19.5 -0.14111674 24
## 8: 22.5 -0.14609825 24
## 9: 25.5 -0.09957072 24
## 10: 28.5 -0.12098146 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.1368209 0.2153795 -0.10692408 213 3.2e-08 ***
## 2: 4.5 0.2964453 0.4306730 -0.15011494 213 3e-09 ***
## 3: 7.5 0.3333333 0.4960787 -0.16696074 213 9.6e-11 ***
## 4: 10.5 0.3521127 0.4942637 -0.14997408 213 5.9e-09 ***
## 5: 13.5 0.3876593 0.4785235 -0.09232594 213 1e-04 ***
## 6: 16.5 0.3970490 0.4910775 -0.09082835 213 0.00014 ***
## 7: 19.5 0.3816231 0.4794597 -0.10563208 213 1.2e-07 ***
## 8: 22.5 0.4084507 0.5148548 -0.10674468 213 8.5e-05 ***
## 9: 25.5 0.4151576 0.4815308 -0.06349500 213 0.011 *
## 10: 28.5 0.3474178 0.4938488 -0.15316819 213 1.4e-08 ***
## time error.diff shapes
## 1: 1.5 -0.10692408 24
## 2: 4.5 -0.15011494 24
## 3: 7.5 -0.16696074 24
## 4: 10.5 -0.14997408 24
## 5: 13.5 -0.09232594 24
## 6: 16.5 -0.09082835 24
## 7: 19.5 -0.10563208 24
## 8: 22.5 -0.10674468 24
## 9: 25.5 -0.06349500 24
## 10: 28.5 -0.15316819 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2149292 0.2543626 -0.07057268 222 0.0012 **
## 2: 4.5 0.3558559 0.3238165 0.03016309 222 0.24 :(
## 3: 7.5 0.3873874 0.4029150 -0.01604677 222 0.54 :(
## 4: 10.5 0.3925354 0.4338872 -0.03958987 222 0.1 :(
## 5: 13.5 0.4118404 0.4496167 -0.04062829 222 0.11 :(
## 6: 16.5 0.4227799 0.4766409 -0.05128067 222 0.032 *
## 7: 19.5 0.4111969 0.4797636 -0.07613895 222 0.0041 **
## 8: 22.5 0.3880309 0.4387192 -0.05567027 222 0.025 *
## 9: 25.5 0.4182754 0.4588002 -0.04151817 222 0.098 .
## 10: 28.5 0.4427284 0.4780249 -0.03297758 222 0.24 :(
## time error.diff shapes
## 1: 1.5 -0.07057268 24
## 2: 4.5 0.03016309 16
## 3: 7.5 -0.01604677 16
## 4: 10.5 -0.03958987 16
## 5: 13.5 -0.04062829 16
## 6: 16.5 -0.05128067 24
## 7: 19.5 -0.07613895 24
## 8: 22.5 -0.05567027 24
## 9: 25.5 -0.04151817 16
## 10: 28.5 -0.03297758 16
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64070 -0.29956 -0.02893 0.31784 0.69906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07365 0.02846 2.588 0.00973 **
## timeNorm 0.03582 0.02788 1.285 0.19900
## obj.diff -0.42758 0.04795 -8.917 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1143242)
##
## Null deviance: 211.41 on 1769 degrees of freedom
## Residual deviance: 202.01 on 1767 degrees of freedom
## AIC: 1189.4
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65216 -0.30312 -0.03606 0.30067 0.70453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05837 0.01763 3.310 0.000943 ***
## timeNorm 0.08051 0.02040 3.947 8.07e-05 ***
## obj.diff -0.42170 0.03315 -12.722 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1116797)
##
## Null deviance: 389.96 on 3329 degrees of freedom
## Residual deviance: 371.56 on 3327 degrees of freedom
## AIC: 2155.4
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61482 -0.25581 -0.02273 0.26062 0.68726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12404 0.01869 6.637 4.39e-11 ***
## timeNorm -0.03577 0.03222 -1.110 0.267
## obj.diff -0.40591 0.04638 -8.753 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09536639)
##
## Null deviance: 163.52 on 1589 degrees of freedom
## Residual deviance: 151.35 on 1587 degrees of freedom
## AIC: 780.68
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2857143 0.4423894 -0.16573403 177 3.1e-08 ***
## 2: 4.5 0.3922518 0.4896586 -0.09931788 177 0.00068 ***
## 3: 7.5 0.3979015 0.5068043 -0.10894563 177 0.00018 ***
## 4: 10.5 0.3454399 0.4841123 -0.14127326 177 3.9e-07 ***
## 5: 13.5 0.3761098 0.4789837 -0.10454587 177 4e-04 ***
## 6: 16.5 0.3825666 0.4794269 -0.09760148 177 0.00024 ***
## 7: 19.5 0.3373688 0.4690341 -0.14526558 177 2.2e-06 ***
## 8: 22.5 0.3704600 0.4630634 -0.09670615 177 0.00066 ***
## 9: 25.5 0.3672316 0.4363366 -0.07355281 177 0.011 *
## 10: 28.5 0.3656174 0.4653488 -0.10252754 177 0.00058 ***
## time error.diff shapes
## 1: 1.5 -0.16573403 24
## 2: 4.5 -0.09931788 24
## 3: 7.5 -0.10894563 24
## 4: 10.5 -0.14127326 24
## 5: 13.5 -0.10454587 24
## 6: 16.5 -0.09760148 24
## 7: 19.5 -0.14526558 24
## 8: 22.5 -0.09670615 24
## 9: 25.5 -0.07355281 24
## 10: 28.5 -0.10252754 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2183612 0.3061629 -0.11151391 333 2.9e-09 ***
## 2: 4.5 0.3389103 0.4353812 -0.10425411 333 4.6e-07 ***
## 3: 7.5 0.3474903 0.4722862 -0.12798535 333 5.2e-09 ***
## 4: 10.5 0.3603604 0.4737386 -0.11826590 333 4.1e-08 ***
## 5: 13.5 0.3706564 0.4485476 -0.08256362 333 5.3e-05 ***
## 6: 16.5 0.3865294 0.4681791 -0.08059753 333 6e-05 ***
## 7: 19.5 0.3912484 0.4725740 -0.08768249 333 4e-06 ***
## 8: 22.5 0.3813814 0.4750074 -0.09779339 333 3.6e-06 ***
## 9: 25.5 0.4268554 0.4779570 -0.05324531 333 0.016 *
## 10: 28.5 0.3792364 0.4684390 -0.08597398 333 6e-06 ***
## time error.diff shapes
## 1: 1.5 -0.11151391 24
## 2: 4.5 -0.10425411 24
## 3: 7.5 -0.12798535 24
## 4: 10.5 -0.11826590 24
## 5: 13.5 -0.08256362 24
## 6: 16.5 -0.08059753 24
## 7: 19.5 -0.08768249 24
## 8: 22.5 -0.09779339 24
## 9: 25.5 -0.05324531 24
## 10: 28.5 -0.08597398 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.1329739 0.1356034 -0.04799946 159 0.0086 **
## 2: 4.5 0.2740341 0.2330713 0.02295544 159 0.35 :(
## 3: 7.5 0.3665768 0.3490692 0.01576858 159 0.59 :(
## 4: 10.5 0.3872417 0.4124680 -0.02740123 159 0.31 :(
## 5: 13.5 0.3980234 0.4736736 -0.08042557 159 0.0021 **
## 6: 16.5 0.4025157 0.4944921 -0.08989010 159 0.0011 **
## 7: 19.5 0.3737646 0.4911595 -0.11967220 159 1.8e-05 ***
## 8: 22.5 0.3827493 0.4974522 -0.11798555 159 8e-05 ***
## 9: 25.5 0.4016173 0.5042757 -0.10492584 159 0.00099 ***
## 10: 28.5 0.3773585 0.5179459 -0.14347107 159 1.5e-05 ***
## time error.diff shapes
## 1: 1.5 -0.04799946 24
## 2: 4.5 0.02295544 16
## 3: 7.5 0.01576858 16
## 4: 10.5 -0.02740123 16
## 5: 13.5 -0.08042557 24
## 6: 16.5 -0.08989010 24
## 7: 19.5 -0.11967220 24
## 8: 22.5 -0.11798555 24
## 9: 25.5 -0.10492584 24
## 10: 28.5 -0.14347107 24
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.43457 -0.35583 -0.02824 0.36659 0.56412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14151 0.13753 1.029 0.30435
## timeNorm 0.04168 0.08032 0.519 0.60420
## obj.diff -0.65302 0.20479 -3.189 0.00158 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1326164)
##
## Null deviance: 41.293 on 299 degrees of freedom
## Residual deviance: 39.387 on 297 degrees of freedom
## AIC: 250.26
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3047619 0.6877076 -0.3682682 30 0.00011 ***
## 2: 4.5 0.4285714 0.5945163 -0.1791742 30 0.08 .
## 3: 7.5 0.3857143 0.5886926 -0.2205960 30 0.04 *
## 4: 10.5 0.3571429 0.5510796 -0.1861968 30 0.014 *
## 5: 13.5 0.3666667 0.5320759 -0.1609103 30 0.016 *
## 6: 16.5 0.3238095 0.5154227 -0.2109705 30 0.0058 **
## 7: 19.5 0.2952381 0.5359163 -0.2485029 30 0.0022 **
## 8: 22.5 0.3523810 0.5143672 -0.1878073 30 0.0093 **
## 9: 25.5 0.3714286 0.4954621 -0.1366127 30 0.052 .
## 10: 28.5 0.3619048 0.5099490 -0.1453273 30 0.05 .
## time error.diff shapes
## 1: 1.5 -0.3682682 24
## 2: 4.5 -0.1791742 16
## 3: 7.5 -0.2205960 24
## 4: 10.5 -0.1861968 24
## 5: 13.5 -0.1609103 24
## 6: 16.5 -0.2109705 24
## 7: 19.5 -0.2485029 24
## 8: 22.5 -0.1878073 24
## 9: 25.5 -0.1366127 16
## 10: 28.5 -0.1453273 24## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.47424 -0.30992 -0.01402 0.28477 0.61857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11471 0.03823 3.001 0.00274 **
## timeNorm 0.04259 0.02942 1.448 0.14788
## obj.diff -0.57219 0.07270 -7.871 6.79e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1057341)
##
## Null deviance: 161.99 on 1469 degrees of freedom
## Residual deviance: 155.11 on 1467 degrees of freedom
## AIC: 873.84
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3080661 0.4453946 -0.14345456 147 9.3e-07 ***
## 2: 4.5 0.3362488 0.4739898 -0.14005322 147 1.2e-06 ***
## 3: 7.5 0.3479106 0.4707060 -0.12577393 147 7e-05 ***
## 4: 10.5 0.3168124 0.4718000 -0.15743110 147 1.6e-07 ***
## 5: 13.5 0.3275024 0.4718746 -0.14858936 147 1.2e-06 ***
## 6: 16.5 0.3294461 0.4713130 -0.14395563 147 3.2e-06 ***
## 7: 19.5 0.3177843 0.4401750 -0.12309216 147 7.2e-06 ***
## 8: 22.5 0.3245870 0.4501494 -0.14007727 147 1.6e-05 ***
## 9: 25.5 0.3819242 0.4605053 -0.08422399 147 0.015 *
## 10: 28.5 0.3459670 0.4466711 -0.08846893 147 0.00035 ***
## time error.diff shapes
## 1: 1.5 -0.14345456 24
## 2: 4.5 -0.14005322 24
## 3: 7.5 -0.12577393 24
## 4: 10.5 -0.15743110 24
## 5: 13.5 -0.14858936 24
## 6: 16.5 -0.14395563 24
## 7: 19.5 -0.12309216 24
## 8: 22.5 -0.14007727 24
## 9: 25.5 -0.08422399 24
## 10: 28.5 -0.08846893 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5243 -0.2887 0.0170 0.2472 0.6074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19432 0.03803 5.110 4.42e-07 ***
## timeNorm -0.16087 0.05554 -2.897 0.003919 **
## obj.diff -0.38512 0.10925 -3.525 0.000458 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09099729)
##
## Null deviance: 56.480 on 569 degrees of freedom
## Residual deviance: 51.595 on 567 degrees of freedom
## AIC: 256.33
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2305764 0.2345181 -0.022364755 57 0.59 :(
## 2: 4.5 0.3759398 0.3083734 0.059567775 57 0.083 .
## 3: 7.5 0.4335840 0.3598460 0.088313450 57 0.068 .
## 4: 10.5 0.4085213 0.3778445 0.026989430 57 0.57 :(
## 5: 13.5 0.3533835 0.3928473 -0.037906228 57 0.35 :(
## 6: 16.5 0.4185464 0.4250343 -0.003127012 57 0.94 :(
## 7: 19.5 0.3734336 0.5099102 -0.128686755 57 0.0067 **
## 8: 22.5 0.3859649 0.5363487 -0.151942393 57 0.0033 **
## 9: 25.5 0.3934837 0.5191794 -0.124405638 57 0.013 *
## 10: 28.5 0.2982456 0.4989451 -0.208899524 57 0.00012 ***
## time error.diff shapes
## 1: 1.5 -0.022364755 16
## 2: 4.5 0.059567775 16
## 3: 7.5 0.088313450 16
## 4: 10.5 0.026989430 16
## 5: 13.5 -0.037906228 16
## 6: 16.5 -0.003127012 16
## 7: 19.5 -0.128686755 24
## 8: 22.5 -0.151942393 24
## 9: 25.5 -0.124405638 24
## 10: 28.5 -0.208899524 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7257 -0.2678 -0.1262 0.3162 0.7323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01988 0.03634 0.547 0.585
## timeNorm 0.04587 0.04484 1.023 0.307
## obj.diff -0.28844 0.06270 -4.600 4.99e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1187679)
##
## Null deviance: 87.696 on 719 degrees of freedom
## Residual deviance: 85.157 on 717 degrees of freedom
## AIC: 514.24
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2281746 0.3097399 -0.10902976 72 0.038 *
## 2: 4.5 0.3630952 0.4426004 -0.08561359 72 0.053 .
## 3: 7.5 0.3630952 0.4724575 -0.11685188 72 0.0061 **
## 4: 10.5 0.3392857 0.4525641 -0.12298405 72 0.0052 **
## 5: 13.5 0.3670635 0.4656216 -0.09769915 72 0.047 *
## 6: 16.5 0.4087302 0.4811146 -0.06977024 72 0.061 .
## 7: 19.5 0.3392857 0.4360550 -0.11830674 72 0.011 *
## 8: 22.5 0.4365079 0.4764586 -0.03047354 72 0.52 :(
## 9: 25.5 0.3769841 0.4251328 -0.04729018 72 0.27 :(
## 10: 28.5 0.3710317 0.4756434 -0.11448523 72 0.021 *
## time error.diff shapes
## 1: 1.5 -0.10902976 24
## 2: 4.5 -0.08561359 16
## 3: 7.5 -0.11685188 24
## 4: 10.5 -0.12298405 24
## 5: 13.5 -0.09769915 24
## 6: 16.5 -0.06977024 16
## 7: 19.5 -0.11830674 24
## 8: 22.5 -0.03047354 16
## 9: 25.5 -0.04729018 16
## 10: 28.5 -0.11448523 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70507 -0.22068 -0.04335 0.24408 0.76625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06325 0.02388 -2.648 0.00820 **
## timeNorm 0.08543 0.03269 2.613 0.00909 **
## obj.diff -0.23906 0.04135 -5.782 9.53e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09501969)
##
## Null deviance: 111.35 on 1139 degrees of freedom
## Residual deviance: 108.04 on 1137 degrees of freedom
## AIC: 556.99
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.09899749 0.1876252 -0.11039497 114 1.2e-07 ***
## 2: 4.5 0.28446115 0.4616587 -0.18406741 114 2.4e-08 ***
## 3: 7.5 0.30325815 0.5145617 -0.21299544 114 2.4e-09 ***
## 4: 10.5 0.33583960 0.4960885 -0.16726069 114 2e-06 ***
## 5: 13.5 0.34085213 0.4349206 -0.09783900 114 0.00087 ***
## 6: 16.5 0.37468672 0.4700852 -0.09179220 114 0.0085 **
## 7: 19.5 0.41102757 0.5126837 -0.10061540 114 4.9e-06 ***
## 8: 22.5 0.39097744 0.5324474 -0.14712519 114 0.00012 ***
## 9: 25.5 0.44987469 0.5092968 -0.05935521 114 0.1 :(
## 10: 28.5 0.32957393 0.4942599 -0.17055244 114 9.2e-07 ***
## time error.diff shapes
## 1: 1.5 -0.11039497 24
## 2: 4.5 -0.18406741 24
## 3: 7.5 -0.21299544 24
## 4: 10.5 -0.16726069 24
## 5: 13.5 -0.09783900 24
## 6: 16.5 -0.09179220 24
## 7: 19.5 -0.10061540 24
## 8: 22.5 -0.14712519 24
## 9: 25.5 -0.05935521 16
## 10: 28.5 -0.17055244 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70375 -0.19717 -0.02273 0.23861 0.72796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02461 0.04370 -0.563 0.574
## timeNorm -0.08556 0.06838 -1.251 0.212
## obj.diff -0.08977 0.07434 -1.208 0.228
##
## (Dispersion parameter for gaussian family taken to be 0.09043054)
##
## Null deviance: 24.581 on 269 degrees of freedom
## Residual deviance: 24.145 on 267 degrees of freedom
## AIC: 122.35
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.05291005 0.08093653 -0.05322518 27 0.0059 **
## 2: 4.5 0.16931217 0.26803793 -0.14051200 27 0.036 *
## 3: 7.5 0.38095238 0.48102915 -0.09893471 27 0.19 :(
## 4: 10.5 0.45502646 0.59775767 -0.15065745 27 0.052 .
## 5: 13.5 0.64021164 0.69702994 -0.05290538 27 0.32 :(
## 6: 16.5 0.46031746 0.60627919 -0.13542918 27 0.0082 **
## 7: 19.5 0.37037037 0.45492616 -0.09534505 27 0.16 :(
## 8: 22.5 0.40740741 0.54296476 -0.10429981 27 0.014 *
## 9: 25.5 0.37037037 0.51469153 -0.14376601 27 0.046 *
## 10: 28.5 0.35978836 0.54066115 -0.19060030 27 0.028 *
## time error.diff shapes
## 1: 1.5 -0.05322518 24
## 2: 4.5 -0.14051200 24
## 3: 7.5 -0.09893471 16
## 4: 10.5 -0.15065745 16
## 5: 13.5 -0.05290538 16
## 6: 16.5 -0.13542918 24
## 7: 19.5 -0.09534505 16
## 8: 22.5 -0.10429981 24
## 9: 25.5 -0.14376601 24
## 10: 28.5 -0.19060030 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.50249 -0.31192 0.00158 0.27300 0.57238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18310 0.05115 3.580 0.000366 ***
## timeNorm -0.01421 0.04068 -0.349 0.726902
## obj.diff -0.58809 0.08998 -6.536 1.17e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1006514)
##
## Null deviance: 79.537 on 749 degrees of freedom
## Residual deviance: 75.187 on 747 degrees of freedom
## AIC: 411.33
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3333333 0.4716057 -0.14568003 75 0.00049 ***
## 2: 4.5 0.4057143 0.4928915 -0.09166913 75 0.041 *
## 3: 7.5 0.4361905 0.5070220 -0.06921125 75 0.1 :(
## 4: 10.5 0.3466667 0.4876115 -0.14592638 75 0.00062 ***
## 5: 13.5 0.3885714 0.4705744 -0.08546184 75 0.042 *
## 6: 16.5 0.3809524 0.4634084 -0.08072180 75 0.037 *
## 7: 19.5 0.3523810 0.4739411 -0.13894639 75 0.0027 **
## 8: 22.5 0.3142857 0.4296825 -0.12873142 75 0.0014 **
## 9: 25.5 0.3561905 0.4234421 -0.07931452 75 0.063 .
## 10: 28.5 0.3619048 0.4376259 -0.07920488 75 0.068 .
## time error.diff shapes
## 1: 1.5 -0.14568003 24
## 2: 4.5 -0.09166913 24
## 3: 7.5 -0.06921125 16
## 4: 10.5 -0.14592638 24
## 5: 13.5 -0.08546184 24
## 6: 16.5 -0.08072180 24
## 7: 19.5 -0.13894639 24
## 8: 22.5 -0.12873142 24
## 9: 25.5 -0.07931452 16
## 10: 28.5 -0.07920488 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6842 -0.3690 0.0772 0.3315 0.5546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22719 0.03565 6.373 3.31e-10 ***
## timeNorm 0.12666 0.04933 2.568 0.0104 *
## obj.diff -0.59514 0.07771 -7.659 6.09e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1277274)
##
## Null deviance: 99.075 on 719 degrees of freedom
## Residual deviance: 91.581 on 717 degrees of freedom
## AIC: 566.61
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2242063 0.2095827 -0.02888658 72 0.68 :(
## 2: 4.5 0.4305556 0.3149495 0.11381852 72 0.0088 **
## 3: 7.5 0.4166667 0.4085764 0.01184582 72 0.84 :(
## 4: 10.5 0.4880952 0.4423093 0.05303055 72 0.34 :(
## 5: 13.5 0.5059524 0.4224978 0.09425537 72 0.053 .
## 6: 16.5 0.5218254 0.4587626 0.05950609 72 0.14 :(
## 7: 19.5 0.5099206 0.4752146 0.04435498 72 0.43 :(
## 8: 22.5 0.4821429 0.4348125 0.04340068 72 0.23 :(
## 9: 25.5 0.4821429 0.4639661 0.02021695 72 0.66 :(
## 10: 28.5 0.5257937 0.4719986 0.07500839 72 0.15 :(
## time error.diff shapes
## 1: 1.5 -0.02888658 16
## 2: 4.5 0.11381852 24
## 3: 7.5 0.01184582 16
## 4: 10.5 0.05303055 16
## 5: 13.5 0.09425537 16
## 6: 16.5 0.05950609 16
## 7: 19.5 0.04435498 16
## 8: 22.5 0.04340068 16
## 9: 25.5 0.02021695 16
## 10: 28.5 0.07500839 16## Warning: Removed 1 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.52214 -0.26501 -0.02093 0.25379 0.68368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13267 0.02547 5.209 2.46e-07 ***
## timeNorm 0.17621 0.05107 3.451 0.00059 ***
## obj.diff -0.72141 0.07276 -9.915 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09487992)
##
## Null deviance: 81.624 on 749 degrees of freedom
## Residual deviance: 70.875 on 747 degrees of freedom
## AIC: 367.04
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.08761905 0.08010825 -0.052761161 75 0.023 *
## 2: 4.5 0.23428571 0.16325372 0.044004349 75 0.22 :(
## 3: 7.5 0.31047619 0.29337315 0.004158512 75 0.93 :(
## 4: 10.5 0.34666667 0.37207770 -0.026222602 75 0.49 :(
## 5: 13.5 0.34476190 0.45469333 -0.114025675 75 0.0029 **
## 6: 16.5 0.36952381 0.50703661 -0.142309890 75 0.0015 **
## 7: 19.5 0.37523810 0.48995306 -0.127835397 75 0.0033 **
## 8: 22.5 0.37142857 0.45150631 -0.089772558 75 0.072 .
## 9: 25.5 0.41904762 0.48919916 -0.072377500 75 0.15 :(
## 10: 28.5 0.44380952 0.52420913 -0.082245022 75 0.15 :(
## time error.diff shapes
## 1: 1.5 -0.052761161 24
## 2: 4.5 0.044004349 16
## 3: 7.5 0.004158512 16
## 4: 10.5 -0.026222602 16
## 5: 13.5 -0.114025675 24
## 6: 16.5 -0.142309890 24
## 7: 19.5 -0.127835397 24
## 8: 22.5 -0.089772558 16
## 9: 25.5 -0.072377500 16
## 10: 28.5 -0.082245022 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.69707 -0.28435 -0.01684 0.28245 0.79371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04093 0.01847 -2.217 0.0267 *
## est.confidence.norm -0.14331 0.03231 -4.436 9.59e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1121776)
##
## Null deviance: 264.48 on 2339 degrees of freedom
## Residual deviance: 262.27 on 2338 degrees of freedom
## AIC: 1525.5
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.83760 -0.21568 -0.02533 0.23895 0.82506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.12278 0.01774 -6.919 5.99e-12 ***
## est.confidence.norm 0.02176 0.03276 0.664 0.507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1054863)
##
## Null deviance: 224.52 on 2129 degrees of freedom
## Residual deviance: 224.47 on 2128 degrees of freedom
## AIC: 1257.9
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.85522 -0.27519 -0.02402 0.27775 0.80100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02339 0.01675 -1.396 0.163
## est.confidence.norm -0.02606 0.03348 -0.778 0.436
##
## (Dispersion parameter for gaussian family taken to be 0.1210069)
##
## Null deviance: 268.47 on 2219 degrees of freedom
## Residual deviance: 268.39 on 2218 degrees of freedom
## AIC: 1615.7
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 3391.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.06594 -0.71512 -0.03989 0.71176 3.10382
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02224 0.1491
## Residual 0.09362 0.3060
## Number of obs: 6690, groups: IDjoueur, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05191 0.01942 128.00000 -2.673 0.008503 **
## est.confidence.norm -0.07215 0.01859 6685.00000 -3.881 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.473
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: 446.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9914 -0.6545 0.0126 0.5910 3.1773
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.04937 0.2222
## Residual 0.06347 0.2519
## Number of obs: 2340, groups: IDjoueur, 78
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06000 0.03076 153.00000 -1.951 0.052890 .
## est.confidence.norm -0.10732 0.03191 2337.70000 -3.363 0.000782 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.550
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 1102.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.60773 -0.66645 -0.04568 0.67995 2.81924
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01313 0.1146
## Residual 0.09256 0.3042
## Number of obs: 2130, groups: IDjoueur, 71
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.164e-01 2.411e-02 3.078e+02 -4.827 2.19e-06 ***
## est.confidence.norm 8.855e-03 3.778e-02 1.710e+03 0.234 0.815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.779
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 1105.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2414 -0.7062 -0.0089 0.6856 3.1920
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.03321 0.1822
## Residual 0.08827 0.2971
## Number of obs: 2220, groups: IDjoueur, 74
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.217e-02 2.779e-02 1.680e+02 -1.158 0.249
## est.confidence.norm -6.521e-03 3.749e-02 2.154e+03 -0.174 0.862
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.606